AI-Driven Adaptive Optimization for Autonomous Control Systems: Advances, Challenges, and Industrial Applications

Abstract

Corresponding Author: Li Wei; Email: Abstract: Autonomous control systems are increasingly integrated into industrial production, smart cities, and robotics, demanding higher adaptability to complex and dynamic environments. This study explores the application of artificial intelligence (AI) technologies, including deep reinforcement learning and fuzzy logic, in adaptive optimization of autonomous control systems. It analyzes recent advances, addresses key challenges such as real-time response and robustness, and verifies effectiveness through industrial case studies. The results show AI-driven strategies significantly improve control precision and system adaptability. This paper provides insights for future research on intelligent autonomous control, promoting its sustainable development in diverse fields.

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